Agricultural residues from straw are widely used for energy and other applications. The thermal conductivity is one of the most important thermophysical properties considered when using agricultural residues, such as rice straw, in renewable energy engineering. In this paper, the thermal conductivity of rice straw was measured using a thermal probe by the transient hot wire method at selected moisture contents, temperatures and dry densities. The moisture contents of the samples ranged from 0 to 21.47 percent wet basis and the dry densities ranged from 90.7 to 136.4 kg/m3 and the temperature ranged from 0 to 170°C. Under those conditions, the thermal conductivity was measured and analyzed. Experiment results showed that the thermal conductivity increases with the increases of the density, moisture content and temperature, and the relationship among them is approximately described in a linear way. A new model to predict the thermal conductivity of agricultural residues from straw was proposed. The calculated results by the proposed model are in good agreement with the experimental data.

Ceramic culture as Chinese culture has a long history and is the Chinese people’s spiritual home. The ceramic culture resource base contains a large number of images, videos, and other resources, so the collection of image category of data mining, as well as the finishing processing, is very critical. Thousands of years of ceramic data accumulation and impatient information demand have created a new point of contradiction for ceramic cultural resources. Therefore, in order to address this issue, we carried out a research study based on the concept of big data mining of ceramic cultural resource data, which is based on data fusion and feature extraction methods. We also considered semantic segmentation processing methods, which are used for data information management, scheduling, identification, collection, statistics, and aggregation of heterogeneous ceramic cultural big data. Further, a fine mining method for ceramic culture big data based on semantic segmentation is also proposed. As a result, the distributed storage of information and detection capability of ceramic cultural resource data are improved. The experimental results reveal that the proposed method performed better than the earlier approaches.